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167 results for “cancer driver”
Protein structure files for the paper "Multiplexed identification of RAS paralog imbalance as a driver of lung cancer growth" in Nature Cell Biology by Tang et al.
<p>This archive contains models of HRAS, KRAS, and NRAS homo- and heterodimers with various mutations discussed in the paper, "Multiplexed identification of RAS paralog imbalance as a driver of lung cancer growth" in Nature Cell Biology by Tang et al.<br> as well as crystallographic dimers of these proteins as identified by the ProtCAD database, http://dunbrack2.fccc.edu/ProtCAD/Results/PfamArchClusterInfo.aspx?GroupId=8 (cluster 5). Several of the models are shown in Supp. Figure 11b and the crystallographic dimers of RAS that provide evidence for the possible biological relevance of these models are shown in Supp. Figure 11a.</p> <p>The crystallographic dimers were identified by clustering all possible interfaces generated by symmetry operators in crystals of HRAS, KRAS, and NRAS as described in the paper: Xu, Q., Dunbrack, R.L. ProtCID: a data resource for structural information on protein interactions. <em>Nat Commun</em> <strong>11</strong>, 711 (2020). https://doi.org/10.1038/s41467-020-14301-4.</p> <p>The models were created by superposing monomers of HRAS, KRAS, or NRAS onto the alpha4-alpha5 dimer present in the crystal of PDB entry 3k8y. Mutations were made in PyMOL. The structures were relaxed with the FastRelax protocol and the Ref2015 scoring function in the program Rosetta, which uses the backbone-dependent rotamer library of Shapovalov and Dunbrack to repack side chains.</p> <p>The crystallographic dimers are contained in a zipped PyMOL session. The mmCIF format for all the structures is present in a zip file, Tang_et_al_crystallographic_and_modeled_RAS_dimer_ciffiles.zip. The PyMOL session and zip file contains 87 HRAS dimers, 14 KRAS dimers, and 1 NRAS dimer, all having the interface consisting of the alpha4 and alpha5 helices. The PyMOL session also contains the modeled structures. Only Mg ions and GTP/GNP/GDP ligands are shown. Others are present but hidden and may be displayed by PyMOL ("show sticks, het").</p> <p> </p>
CRISPR/dCas9-mediated DNA demethylation screen identifies driver epigenetic determinants of colorectal cancer (Processed data)
<p><strong>Background:</strong> Promoter hypermethylation of tumour suppressor genes is frequently observed during the malignant transformation of colorectal cancer (CRC). However, whether this epigenetic mechanism is an actual driver of cancer or is a mere consequence of the carcinogenic process remains to be elucidated.</p> <p><strong>Results: </strong>In this work we performed an integrative multi -omic approach to identify gene candidates with strong correlations between DNA methylation and gene expression in human CRC samples and a set of 8 colon cancer cell lines. As a proof of concept, we combined recent CRISPR-Cas9 epigenome editing tools (dCas9-TET1, dCas9-TET-IM) with a custom arrayed gRNA library to modulate the DNA methylation status of 56 promoters previously linked with strong epigenetic repression in CRC, and we monitored the potential functional consequences of such DNA methylation loss by means of a high-content cell proliferation screen. Overall, the epigenetic modulation of most of these DNA methylated regions had a mild impact in the reactivation of gene expression and in the viability of cancer cells. Interestingly, we found that epigenetic reactivation of RSPO2 in the tumour context was associated with a significant impairment in cell proliferation in p53-/- cancer cell lines and further validation with human samples demonstrated that the epigenetic silencing of RSPO2 is a mid-late event in the adenoma to carcinoma sequence.</p> <p><strong>Conclusions: </strong>These results highlight the potential role of DNA methylation as a driver mechanism of CRC and open up the venue for the identification of novel therapeutic windows based on the epigenetic reactivation of certain tumour suppressor genes.</p>
Analyses of human cancer driver genes uncovers evolutionarily conserved RNA structural elements involved in posttranscriptional control - associated datasets
<p>These datasets include raw data output and associated files from ScanFold and CMbuilder analyses of human cancer driver gene mRNA. ScanFold was used to predict RNA regions of unusual thermodynamic stability, and CMbuilder was used to evaluate covariation of the predicted structures in those regions. Please view the file <em>README_general_description_Zenodo_files.txt</em> for brief descriptions of the content.</p> <p>These data are associated with the manuscript entitled <em>Analyses of human cancer driver genes uncovers evolutionarily conserved RNA structural elements involved in posttranscriptional control</em>. The manuscript has currently been submitted for review in PLOS ONE.</p>
Tables for Finding Driver Mutations in Cancer: Elucidating the Role of Background Mutational Processes
<p>nucleotide and codon tables used for plots and figures and analysis, have all mutations in 520 cancer genes. </p> <p><strong>Table S2. </strong>Correlation between mutability and recurrence of mutations in cancer-associated genes.</p> <p><strong>Table S3. </strong>List of cancer-associated genes and GO terms.</p>
Cancer Health Disparities drivers with BERTopic Modelling and PyCaret Evaluation - Text data
<p>The complex interplay of social, behavioral, lifestyle, environmental, health system, and natural health variables contribute to disparities in cancer treatment across racial and ethnic groups. Consequently, it is necessary to identify the variables contributing to cancer health inequalities and develop strategies to achieve health equality. PubMed abstract on Cancer health disparities was scraped with a bio.Entrez python package. Preprocessed data with regex and Natural tool kit (NLTK), topic modelling with BERTopic embeddings, and c-TF-IDF to construct dense clusters and analyze top topics linked with Cancer health disparities. Model evaluation with PyCaret coherence score and web app deployment with Streamlit. The results showed that Topic 32 with terms obese, female, male, school, survey, student, poet, and discrepancy had the best coherence score of 0.3687. In contrast, topic 8, with terms prevalence, adult, income, high, usage, diabetes, education, elderly, change and low, received the least coherence score of 0.3255. The model classifies each Subject Word score based on the scores, the granular topic concerns and trends related to cancer health disparities, investigates the connection between drivers of cancer health disparities, and evaluates the model with their coherence score values</p>
CarcSeq measurement of lung cancer driver mutations predicts mouse strain- and sex-related incidence of spontaneous lung neoplasia
<p>The data contained herein are the fastq files collected for individual mouse lung DNA samples. Ten samples each of male and female, B6C3F1 and CD-1 mouse lung DNA were analyzed using the CarcSeq method for error-corrected next-generation sequencing as previously described [Harris, KL, Walia, V, Gong, B, et al. Quantification of cancer driver mutations in human breast and lung DNA using targeted, error-corrected CarcSeq. <i>Environ Mol Mutagen</i>. 2020; 61: 872– 889. <a href="https://doi.org/10.1002/em.22409">https://doi.org/10.1002/em.22409</a> and Karen L McKim, Meagan B Myers, Kelly L Harris, Binsheng Gong, Joshua Xu, Barbara L Parsons, CarcSeq Measurement of Rat Mammary Cancer Driver Mutations and Relation to Spontaneous Mammary Neoplasia, <em>Toxicological Sciences</em>, 2021;, kfab040, <a href="https://doi.org/10.1093/toxsci/kfab040">https://doi.org/10.1093/toxsci/kfab040</a>]. A readme text file provides the key to which sample numbers correspond with which mouse strain and sex. All the fastq files within a given folder were used to construct single strand consensus sequences, which were then subject to downstream analyses as reported in the associated publication.</p>
PRospective REgistry of Advanced Stage CancER (PREFER) Patients to Assess Prevalence of Actionable Biomarkers and Driver Mutations to Address Disparities in Precision Medicine
ClinicalTrials.gov study NCT05697198. IPD Sharing: UNDECIDED. Countries: 1. Publications: 14.
Natural Killer(NK) Cell Combined With Programmed Death-1(PD-1) Antibody as Second Line Therapy for Advanced Driver Mutation Negative Non-small Cell Lung Cancer
ClinicalTrials.gov study NCT03958097. IPD Sharing: NO. Countries: 1. Publications: 2.
Assessing Drivers and Barriers to Follow-Up Screening for Anal Cancer in Men Who Have Sex With Men
ClinicalTrials.gov study NCT00501306. IPD Sharing: Not stated. Countries: 1. Publications: 1.
First-Line Treatment of Advanced Non-Small-Cell Lung Cancer With Negative Driver Gene: a Multicenter, Single-Arm Trial
ClinicalTrials.gov study NCT05574998. IPD Sharing: NO. Countries: 1. Publications: 6.
Apatinib Combined With Vinorelbine for Non-driver Gene Mutation Non-small Cell Lung Cancer
ClinicalTrials.gov study NCT03652857. IPD Sharing: Not stated. Countries: 1. Publications: 1.
First-Line Treatment for Advanced Non-squamous Non-Small-Cell Lung Cancer With Negative Driver Gene: a Single-center, Single-Arm Trial
ClinicalTrials.gov study NCT05648071. IPD Sharing: NO. Countries: 1. Publications: 10.
Multicenter Observational Study of Chinese Non-Small Cell Lung Cancer (NSCLC) Patients With Rare Driver Gene Mutation
ClinicalTrials.gov study NCT04137718. IPD Sharing: NO. Countries: 1. Publications: 7.
CarcSeq measurement of lung cancer driver mutations predicts mouse strain- and sex-related incidence of spontaneous lung neoplasia
Open the record for dataset details and reuse information.
Construction and clinical significance of prognostic risk markers based on cancer driver genes in lung adenocarcinoma
Open the record for dataset details and reuse information.
Analysis of Driver Gene Mutation in Early Stage Non-small Cell Lung Cancer
ClinicalTrials.gov study NCT04266691. IPD Sharing: NO. Countries: 0. Publications: 1.
Deep Multiple-omics Profiling of Brain Tumors Identifies Signaling Networks Downstream of Cancer Driver Genes
GEO Series GSE114331. Mus musculus. 18 samples. Type: Expression profiling by high throughput sequencing.
A nested case-control study to investigate drivers for metastatic disease in breast cancer (QC substudy)
GEO Series GSE81954. Homo sapiens. 97 samples. Type: Expression profiling by array; Third-party reanalysis.
Machine learning multi-omics analysis reveals cancer driver dysregulation in pan-cancer cell lines compared to primary tumors
GEO Series GSE213519. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
Transcriptome analysis implicates MYC as a driver of early onset colorectal cancer
GEO Series GSE196006. Homo sapiens. 42 samples. Type: Expression profiling by high throughput sequencing.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.